For training step, the implemented KEIS-BiGRUCNN ensemble approach applied aravec embedding as pre trained model with 300 dimensions for Arabic language that prepared for training step. In contrast, we applied word2vec embedding for English proposed by [1]}, the pre trained embedding avalible at github acount https://github.com/felipebravom/AffectiveTweets/releases/download/1.0.0/w2v.twitter.edinburgh10M.400d.csv.gz with 400 dimensions. Several hyper-parameter have been used for optimization. Table REF  provides more details about the value of each parameter have been used during the training step for both approaches. It's worth mentioning that amsgrad optimizer proposed by [2]} the updated version of adam optimizer with slight enhancement regards to the system performance. The final step, after we obtained the final prediction for each model, the predictions have been multiplied by the best chosen weight to enhance the over all results. The ensemble architecture is shown in Fig. REF  KEIS-BiGRUCNN has been used to solve sub-task A for Arabic language and sub-task B,C for English language.
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